Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection contribute to platelet activation and thrombus formation. Severe coronavirus disease 2019 (COVID-19) is characterized by an increased risk of thromboembolic events that can lead to adverse outcomes in patients with severe disease manifestations. We present the case of a 41-year-old man who died from a pulmonary embolism and review the connection between SARS-CoV-2 infection, increased platelet counts, and the resulting fatal thrombosis. Total knee replacement surgery was performed and the patient was able to ambulate for a few days postoperatively. The platelet count exceeded the upper limit between postoperative days six and nine, reaching 708,000/μL on day 20. SARS-CoV-2 was confirmed 14 days after surgery, and the patient died 23 days after surgery while hospitalized. Autopsy revealed a fatal pulmonary embolism and deep vein thrombosis. If blood clots are caused by increased platelet counts due to COVID-19, it is essential to understand this relationship and prepare for complications that may arise after infection. Several recent studies have shown a link between COVID-19 and coagulation. We propose several considerations for autopsies of unexpected fatal pulmonary embolism during the SARS-CoV-2 endemic period.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection contribute to platelet activation and thrombus formation. Severe coronavirus disease 2019 (COVID-19) is characterized by an increased risk of thromboembolic events that can lead to adverse outcomes in patients with severe disease manifestations. We present the case of a 41-year-old man who died from a pulmonary embolism and review the connection between SARS-CoV-2 infection, increased platelet counts, and the resulting fatal thrombosis. Total knee replacement surgery was performed and the patient was able to ambulate for a few days postoperatively. The platelet count exceeded the upper limit between postoperative days six and nine, reaching 708,000/μL on day 20. SARS-CoV-2 was confirmed 14 days after surgery, and the patient died 23 days after surgery while hospitalized. Autopsy revealed a fatal pulmonary embolism and deep vein thrombosis. If blood clots are caused by increased platelet counts due to COVID-19, it is essential to understand this relationship and prepare for complications that may arise after infection. Several recent studies have shown a link between COVID-19 and coagulation. We propose several considerations for autopsies of unexpected fatal pulmonary embolism during the SARS-CoV-2 endemic period.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection contribute to platelet activation and thrombus formation. Severe coronavirus disease 2019 (COVID-19) is characterized by an increased risk of thromboembolic events that can lead to adverse outcomes in patients with severe disease manifestations. We present the case of a 41-year-old man who died from a pulmonary embolism and review the connection between SARS-CoV-2 infection, increased platelet counts, and the resulting fatal thrombosis. Total knee replacement surgery was performed and the patient was able to ambulate for a few days postoperatively. The platelet count exceeded the upper limit between postoperative days six and nine, reaching 708,000/μL on day 20. SARS-CoV-2 was confirmed 14 days after surgery, and the patient died 23 days after surgery while hospitalized. Autopsy revealed a fatal pulmonary embolism and deep vein thrombosis. If blood clots are caused by increased platelet counts due to COVID-19, it is essential to understand this relationship and prepare for complications that may arise after infection. Several recent studies have shown a link between COVID-19 and coagulation. We propose several considerations for autopsies of unexpected fatal pulmonary embolism during the SARS-CoV-2 endemic period.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection contribute to platelet activation and thrombus formation. Severe coronavirus disease 2019 (COVID-19) is characterized by an increased risk of thromboembolic events that can lead to adverse outcomes in patients with severe disease manifestations. We present the case of a 41-year-old man who died from a pulmonary embolism and review the connection between SARS-CoV-2 infection, increased platelet counts, and the resulting fatal thrombosis. Total knee replacement surgery was performed and the patient was able to ambulate for a few days postoperatively. The platelet count exceeded the upper limit between postoperative days six and nine, reaching 708,000/μL on day 20. SARS-CoV-2 was confirmed 14 days after surgery, and the patient died 23 days after surgery while hospitalized. Autopsy revealed a fatal pulmonary embolism and deep vein thrombosis. If blood clots are caused by increased platelet counts due to COVID-19, it is essential to understand this relationship and prepare for complications that may arise after infection. Several recent studies have shown a link between COVID-19 and coagulation. We propose several considerations for autopsies of unexpected fatal pulmonary embolism during the SARS-CoV-2 endemic period.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Purpose: In this study, we aimed to determine the clinicopathologic, radiologic, and molecular significance of the tumor invasiveness to further stratify the patients with high-grade (HG) upper tract urothelial carcinoma (UTUC) who can be treated less aggressively. Materials and Methods: Clinicopathologic and radiologic characteristics of 166 surgically resected HG UTUC (48 noninvasive, and 118 invasive) cases were evaluated. Six noninvasive UTUC cases with intratumoral tumor grade heterogeneity were selected for whole-exome sequencing (WES) to understand the underlying molecular pathophysiology. Barcode-tagging sequencing was done for validation of the target genes from WES data. Results: Patients with noninvasive UTUC showed no cancer-specific death with better cancer-specific survival (p < 0.001) and recurrence-free survival (p < 0.001) compared to the patients with invasive UTUC. Compared to the invasive UTUC, noninvasive UTUC was correlated to a low grade (LG) on the preoperative abdominal computed tomography (CT) grading system (p < 0.001), histologic intratumoral tumor grade heterogeneity (p=0.018), discrepancy in preoperative urine cytology diagnosis (p=0.018), and absence of urothelial carcinoma in situ (p < 0.001). WES of the heterogeneous components showed mutually shared HRAS and FGFR3 mutations shared between the HG and LG components. HRAS mutation was associated with the lower grade on preoperative abdominal CT and intratumoral tumor grade heterogeneity (p=0.045 and p < 0.001, respectively), whereas FGFR3 mutation was correlated to the absence of carcinoma in situ (p < 0.001). Conclusion According to our comprehensive analysis, HG noninvasive UTUC can be preoperatively suspected based on distinct preoperative radiologic, cytologic, histologic, and molecular features. Noninvasive HG UTUC shows excellent prognosis and thus should be treated less aggressively.

Background: Bladder cancer is characterized by frequent mutations, which provide potential therapeutic targets for most patients. The effectiveness of emerging personalized therapies depends on an accurate molecular diagnosis, for which the accurate estimation of the neoplastic cell percentage (NCP) is a crucial initial step. However, the established method for determining the NCP, manual counting by a pathologist, is time-consuming and not easily executable. Methods: To address this, artificial intelligence (AI) models were developed to estimate the NCP using nine convolutional neural networks and the scanned images of 39 cases of urinary tract cancer. The performance of the AI models was compared to that of six pathologists for 119 cases in the validation cohort. The ground truth value was obtained through multiplexed immunofluorescence. The AI model was then applied to 41 cases in the application cohort that underwent next-generation sequencing testing, and its impact on the copy number variation (CNV) was analyzed. Results: Each AI model demonstrated high reliability, with intraclass correlation coefficients (ICCs) ranging from 0.82 to 0.88. These values were comparable or better to those of pathologists, whose ICCs ranged from 0.78 to 0.91 in urothelial carcinoma cases, both with and without divergent differentiation/ subtypes. After applying AI-driven NCP, 190 CNV (24.2%) were reclassified with 66 (8.4%) and 78 (9.9%) moved to amplification and loss, respectively, from neutral/minor CNV. The neutral/minor CNV proportion decreased by 6%. Conclusions These results suggest that AI models could assist human pathologists in repetitive and cumbersome NCP calculations.

The remarkable advances in our understanding of renal tumor pathogenesis, driven by the widespread application of molecular testing, are reflected in the latest 2022 World Health Organization classification. This updated classification categorizes renal cell carcinoma (RCC) into morphologically and molecularly defined RCCs. It includes updates to existing entities and introduces newly established and provisional entities. A standard macroscopic and microscopic evaluation is typically sufficient for diagnosing morphologically defined RCCs and serves as the initial step in the identification of molecularly defined entities. In cases where classification based solely on histologic examination is challenging, a limited panel of immunohistochemical stains can be employed to aid in the diagnosis, with molecular testing for validation if necessary. Therefore, this review explores the key clinical, pathological, and molecular features essential for classifying both the commonly encountered morphologically defined RCCs and the less common but clinically significant molecularly defined RCCs. The goal is to increase awareness of these RCC subtypes among clinicians and promote a deeper understanding of the pathological diagnostic process, ultimately improving patient care.